User profiles for Yuelyu Ji
Yuelyu JiUniversity of Pittsburgh Verified email at pitt.edu Cited by 82 |
Prediction of covid-19 patients' emergency room revisit using multi-source transfer learning
The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant
severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous …
severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous …
Mitigating the risk of health inequity exacerbated by large language models
Recent advancements in large language models (LLMs) have demonstrated their potential
in numerous medical applications, particularly in automating clinical trial matching for …
in numerous medical applications, particularly in automating clinical trial matching for …
Reasoningrank: Teaching student models to rank through reasoning-based knowledge distillation
Reranking documents based on their relevance to a given query is critical in information
retrieval. Traditional reranking methods often focus on improving the initial rankings but lack …
retrieval. Traditional reranking methods often focus on improving the initial rankings but lack …
Rag-rlrc-laysum at biolaysumm: Integrating retrieval-augmented generation and readability control for layman summarization of biomedical texts
This paper introduces the RAG-RLRC-LaySum framework, designed to make complex
biomedical research understandable to laymen through advanced Natural Language Processing (…
biomedical research understandable to laymen through advanced Natural Language Processing (…
Assertion detection large language model in-context learning lora fine-tuning
In this study, we aim to address the task of assertion detection when extracting medical
concepts from clinical notes, a key process in clinical natural language processing (NLP). …
concepts from clinical notes, a key process in clinical natural language processing (NLP). …
Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering
Multi-hop question answering (QA) requires models to retrieve and reason over multiple
pieces of evidence. While Retrieval-Augmented Generation (RAG) has made progress in this …
pieces of evidence. While Retrieval-Augmented Generation (RAG) has made progress in this …
Assertion detection in clinical natural language processing using large language models
In this study, we aim to address the task of assertion detection when extracting medical
concepts from clinical notes, a key process in clinical natural language processing (NLP). …
concepts from clinical notes, a key process in clinical natural language processing (NLP). …
Towards accurate and clinically meaningful summarization of electronic health record notes: A guided approach
Clinicians are often under time pressure when they review patients’ electronic health
records (EHR), therefore, there are great benefits to providing clinicians high-quality …
records (EHR), therefore, there are great benefits to providing clinicians high-quality …
Transfer learning with clinical concept embeddings from large language models
Knowledge sharing is crucial in healthcare, especially when leveraging data from multiple
clinical sites to address data scarcity, reduce costs, and enable timely interventions. Transfer …
clinical sites to address data scarcity, reduce costs, and enable timely interventions. Transfer …
Curriculum Guided Reinforcement Learning for Efficient Multi Hop Retrieval Augmented Generation
Retrieval-augmented generation (RAG) grounds large language models (LLMs) in up-to-date
external evidence, yet existing multi-hop RAG pipelines still issue redundant subqueries, …
external evidence, yet existing multi-hop RAG pipelines still issue redundant subqueries, …